304 207
Full Length Article
Fusion: Practice and Applications
Volume 14 , Issue 1, PP: 273-282 , 2024 | Cite this article as | XML | Html |PDF

Title

A Data Fusion Approach for Accurate Diagnosis of Parkinson's Disease

  Fredy Cañizares Galarza 1 * ,   Luis Freire Lescano 2 ,   Lina Espinoza Neri 3 ,   Dilafruz Nabieva 4

1  Director de la Universidad Regional Autónoma de los Andes (UNIANDES) Sede Santo Domingo, Ecuador
    (dir.santodomingo@uniandes.edu.ec)

2  Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador
    (ciad@uniandes.edu.ec)

3  Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador
    (ua.linaespinoza@uniandes.edu.ec)

4  Tashkent State University of Economics, Uzbekistan
    (della.nab27@gmail.com)

Full Text Download


Doi   :   https://doi.org/10.54216/FPA.140120

Received: June 27, 2023 Revised: October 12, 2023 Accepted: December 15, 2023

Abstract :

Diagnosing Parkinson's Disease (PD) can be quite challenging as it presents with symptoms and lacks biomarkers. Nevertheless, the use of data fusion, which combines types of data using machine learning techniques holds promise, for the timely detection of the disease. In this study, we explore the application of data fusion by employing Principal Component Analysis (PCA) as a step to reduce complexity. We then utilize the K Nearest Neighbors (KNN) classification to improve the accuracy of PD diagnosis. By analyzing nonlinear features associated with PD from a dataset PCA helps us extract attributes while maintaining important variations in the data. Subsequently, KNN is employed to identify patterns in this reduced feature space and effectively distinguish between individuals with PD and those who are healthy. Our results show improvements when using the KNN classifier compared to state-of-the-art approaches. This demonstrates its effectiveness in detecting PD leading to promising outcomes and providing a framework for precise PD diagnosis.

Keywords :

data fusion; Machine learning; Parkinsonian symptoms; Data-driven diagnosis; Neurological disorder; Pattern recognition techniques; Diagnostic accuracy assessment

References :

[1]    Patwekar, Mohsina, Faheem Patwekar, Syed Sanaullah, Daniyal Shaikh, Ustad Almas, and Rohit Sharma. 2023. “Harnessing Artificial Intelligence for Enhanced Parkinson’s Disease Management: Pathways, Treatment, and Prospects.” Trends in Immunotherapy 7 (2): 2395.

[2]    Rasool, Saad, Ali Husnain, Ayesha Saeed, Ahmad Yousaf Gill, and Hafiz Khawar Hussain. 2023. “Harnessing Predictive Power: Exploring the Crucial Role of Machine Learning in Early Disease Detection.” JURIHUM: Jurnal Inovasi Dan Humaniora 1 (2): 302–15.

[3]    Hickman, Richard A, and Sonja W Scholz. 2023. “Precision Diagnosis and Staging of TDP-43 Proteinopathies: Harnessing the Power of Artificial Intelligence.” Brain, awad175.

[4]    Neto, Osmar Pinto. 2023. “Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson’s Disease: A Comprehensive Study Across Diverse Datasets.” Available at SSRN 4617895.

[5]    Torrado, Juan C, Bettina S Husebo, Heather G Allore, Ane Erdal, Stein E Fæø, Haakon Reithe, Elise Førsund, Charalampos Tzoulis, and Monica Patrascu. 2022. “Digital Phenotyping by Wearable-Driven Artificial Intelligence in Older Adults and People with Parkinson’s Disease: Protocol of the Mixed Method, Cyclic ActiveAgeing Study.” PloS One 17 (10): e0275747.

[6]    Ranson, Janice M, Magda Bucholc, Donald Lyall, Danielle Newby, Laura Winchester, Neil P Oxtoby, Michele Veldsman, et al. 2023. “Harnessing the Potential of Machine Learning and Artificial Intelligence for Dementia Research.” Brain Informatics 10 (1): 6.

[7]    Siddiqui, Shahid S, Sivakumar Loganathan, Venkateswaran R Elangovan, and M Yusuf Ali. 2023. “Artificial Intelligence in Precision Medicine.” In A Handbook of Artificial Intelligence in Drug Delivery, 531–69. Elsevier.

[8]    Gupta, Rohan, Smita Kumari, Anusha Senapati, Rashmi K Ambasta, and Pravir Kumar. 2023. “New Era of Artificial Intelligence and Machine Learning-Based Detection, Diagnosis, and Therapeutics in Parkinson’s Disease.” Ageing Research Reviews, 102013.

[9]    Ray, Partha Pratim, and Poulami Majumder. 2023. “The Potential of ChatGPT to Transform Healthcare and Address Ethical Challenges in Artificial Intelligence-Driven Medicine.” Journal of Clinical Neurology (Seoul, Korea) 19 (5): 509.

[10] Liu, Yukun, Chengxuan Zheng, and Baha Ihnaini. 2023. “Harnessing Transfer Learning for Alzheimer’s Disease Prediction.” In International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022), 12588:243–49.

[11] Torrado Vidal, Juan Carlos, Bettina Elisabeth Franziska Husebø, Heather G Allore, Ane Erdal, Stein Erik Fæø, Haakon Reithe, Elise Førsund, Charalampos Tzoulis, and Monica Patrascu. 2022. “Digital Phenotyping by Wearable-Driven Artificial Intelligence in Older Adults and People with Parkinson’s Disease: Protocol of the Mixed Method, Cyclic ActiveAgeing Study.”

[12] Pradesh, Arunachal. n.d. “SCIENTIFIC DISCOVERIES IN THE AGE OF ARTIFICIAL INTELLIGENCE.”

[13] Pise, Anil Audumbar, Khalid K Almuzaini, Tariq Ahamed Ahanger, Ahmed Farouk, Piyush Kumar Pareek, Stephen Jeswinde Nuagah, and others. 2022. “Enabling Artificial Intelligence of Things (AIoT) Healthcare Architectures and Listing Security Issues.” Computational Intelligence and Neuroscience 2022.

[14] Khalil, N., Elkholy, M. and Eassa, M. (2023) “A Comparative Analysis of Machine Learning Models for Prediction of Chronic Kidney Disease”, Sustainable Machine Intelligence Journal, 5. doi: 10.61185/SMIJ.2023.55103.

[15] Olaniyan, Olugbemi T, Charles O Adetunji, Ayobami Dare, Olorunsola Adeyomoye, Mayowa J Adeniyi, and Alex Enoch. 2023. “Clinical Applications of Deep Learning in Neurology and Its Enhancements with Future Predictions.” In Artificial Intelligence for Neurological Disorders, 209–24. Elsevier.

[16] Krishnan, Gokul, Shiana Singh, Monika Pathania, Siddharth Gosavi, Shuchi Abhishek, Ashwin Parchani, and Minakshi Dhar. 2023. “Artificial Intelligence in Clinical Medicine: Catalyzing a Sustainable Global Healthcare Paradigm.” Frontiers in Artificial Intelligence 6.

[17] Shen, Xuechen, and Katsuhiko Ariga. 2023. “Disease Diagnosis with Chemosensing, Artificial Intelligence, and Prospective Contributions of Nanoarchitectonics.” Chemosensors 11 (10): 528.

[18] Vora, Lalitkumar K, Amol D Gholap, Keshava Jetha, Raghu Raj Singh Thakur, Hetvi K Solanki, and Vivek P Chavda. 2023. “Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design.” Pharmaceutics 15 (7): 1916.

[19] Lim, Ashley Cha Yin, Pragadesh Natarajan, R Dineth Fonseka, Monish Maharaj, and Ralph J Mobbs. 2022. “The Application of Artificial Intelligence and Custom Algorithms with Inertial Wearable Devices for Gait Analysis and Detection of Gait-Altering Pathologies in Adults: A Scoping Review of Literature.” Digital Health 8: 20552076221074130.

[20] Lyall, Donald M, Andrey Kormilitzin, Claire Lancaster, Jose Sousa, Fanny Petermann-Rocha, Christopher Buckley, Eric L Harshfield, et al. 2023. “Artificial Intelligence for Dementia—Applied Models and Digital Health.” Alzheimer’s \& Dementia.

[21] Sahu, Mehar, Rohan Gupta, Rashmi K Ambasta, and Pravir Kumar. 2022. “Artificial Intelligence and Machine Learning in Precision Medicine: A Paradigm Shift in Big Data Analysis.” Progress in Molecular Biology and Translational Science 190 (1): 57–100.

[22] Marks, R. 2023. “Artificial Intelligence and Aging: Potential and Precautions.” MOJ Gerontol Ger 8 (2): 43–48.

[23] Battineni, Gopi, Nalini Chintalapudi, Mohammad Amran Hossain, Giuseppe Losco, Ciro Ruocco, Getu Gamo Sagaro, Enea Traini, Giulio Nittari, and Francesco Amenta. 2022. “Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review.” Bioengineering 9 (8): 370.

[24] Wang, Chan, Tianyiyi He, Hong Zhou, Zixuan Zhang, and Chengkuo Lee. 2023. “Artificial Intelligence Enhanced Sensors-Enabling Technologies to next-Generation Healthcare and Biomedical Platform.” Bioelectronic Medicine 9 (1): 17.

[25] Mohammadi, Aliasghar Tabatabaei, Erfan Ghanbarzadeh, Nadia Ghasemi Darestani, Mohammad Nouri, Monireh Rasoulzadehzali, Maryam Moghadamnia, Shadi Nazarizadeh, Reza Nahavandi, Mehrdad Jahedi, and others. 2023. Advancements in Medical Science and the Pharmaceutical Industry: Artificial Intelligence in Medicine, Regenerative Medicine and Stem Cells, New Developments in Pharmaceuticals (Nano Drugs). Nobel Sciences.

[26] Najjar, Reabal. 2023. “Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging.” Diagnostics 13 (17): 2760.

[27] Albahri, A S, Ali M Duhaim, Mohammed A Fadhel, Alhamzah Alnoor, Noor S Baqer, Laith Alzubaidi, O S Albahri, et al. 2023. “A Systematic Review of Trustworthy and Explainable Artificial Intelligence in Healthcare: Assessment of Quality, Bias Risk, and Data Fusion.” Information Fusion.

[28] Singh, Law Kumar, Munish Khanna, and Rekha Singh. 2023. “Artificial Intelligence Based Medical Decision Support System for Early and Accurate Breast Cancer Prediction.” Advances in Engineering Software 175: 103338.

[29] Ghaffar Nia, Nafiseh, Erkan Kaplanoglu, and Ahad Nasab. 2023. “Evaluation of Artificial Intelligence Techniques in Disease Diagnosis and Prediction.” Discover Artificial Intelligence 3 (1): 5.

Cite this Article as :
Style #
MLA Fredy Cañizares Galarza, Luis Freire Lescano, Lina Espinoza Neri, Dilafruz Nabieva. "A Data Fusion Approach for Accurate Diagnosis of Parkinson's Disease." Fusion: Practice and Applications, Vol. 14, No. 1, 2024 ,PP. 273-282 (Doi   :  https://doi.org/10.54216/FPA.140120)
APA Fredy Cañizares Galarza, Luis Freire Lescano, Lina Espinoza Neri, Dilafruz Nabieva. (2024). A Data Fusion Approach for Accurate Diagnosis of Parkinson's Disease. Journal of Fusion: Practice and Applications, 14 ( 1 ), 273-282 (Doi   :  https://doi.org/10.54216/FPA.140120)
Chicago Fredy Cañizares Galarza, Luis Freire Lescano, Lina Espinoza Neri, Dilafruz Nabieva. "A Data Fusion Approach for Accurate Diagnosis of Parkinson's Disease." Journal of Fusion: Practice and Applications, 14 no. 1 (2024): 273-282 (Doi   :  https://doi.org/10.54216/FPA.140120)
Harvard Fredy Cañizares Galarza, Luis Freire Lescano, Lina Espinoza Neri, Dilafruz Nabieva. (2024). A Data Fusion Approach for Accurate Diagnosis of Parkinson's Disease. Journal of Fusion: Practice and Applications, 14 ( 1 ), 273-282 (Doi   :  https://doi.org/10.54216/FPA.140120)
Vancouver Fredy Cañizares Galarza, Luis Freire Lescano, Lina Espinoza Neri, Dilafruz Nabieva. A Data Fusion Approach for Accurate Diagnosis of Parkinson's Disease. Journal of Fusion: Practice and Applications, (2024); 14 ( 1 ): 273-282 (Doi   :  https://doi.org/10.54216/FPA.140120)
IEEE Fredy Cañizares Galarza, Luis Freire Lescano, Lina Espinoza Neri, Dilafruz Nabieva, A Data Fusion Approach for Accurate Diagnosis of Parkinson's Disease, Journal of Fusion: Practice and Applications, Vol. 14 , No. 1 , (2024) : 273-282 (Doi   :  https://doi.org/10.54216/FPA.140120)
↑